Breathing Life into Data

Back after my oldest son was born, Mo — well, he was about a year and a half old — he was just learning to talk. I have this vivid memory — we were sitting on our back deck, it was the first spring day in Portland — the sun was going down, the sky was full of reds and oranges, it was warm, the air was thick with the scent of daphne. And I turned to him and said “sunset” — and he smiled up at me and said “sunset.” I was so proud of him, and he was so proud of himself. But almost instantly I was devastated. I realized I had taken this incredibly beautiful moment and crammed it into a tiny word.

Sunset.

And that’s what data is. It’s an abstraction — a representation — a distillation. As humans we are now taking moments that are rich, complex, and faceted and cramming them into rows and columns with digital technologies.

But abstractions are nothing new. Our very first cave drawings were abstractions of our daily life. Language, photography, film — everything we use to try to capture and communicate experience will fail. Anything other than living lacks the fidelity.

Like Borges map of 1:1 scale; it was developed by trying to capture the highest fidelity of the land, but became useless as it approached that richness of detail. But only because, in order to use the map, you had to spread it across the land it represented. The same would hold true for language. If you had to read every detail of what a sunset looked and felt like it would be too cumbersome to hold one’s attention.

But we’re not capturing data for our own consumption. It’s food for algorithms. And the machines that run them never grow weary. They could read every detail of every sunset ever experienced and learn about every cultural nuance, atmospheric ramification, and have a new understanding of sunset that we can’t achieve as singular beings.

This is why we need to breathe life back into data. We distilled it in the first place because we thought we just needed basic facts for simple decision-making. But as we rely more and more on machines to make decisions with bigger and broader implications, we need more fidelity. We need the machine to see the details, see around the periphery, and see the things we think have no bearing.